This kind of approach significantly differs from the extensions of GNNs that have been proposed in the literature, in the last decade, to handle tree structures. Architectures such as Tree-LSTMs [1]} and TreeNet [2]} are basically designed so as to compute tree representations in a bottom-up fashion, by aggregating in a given node the contributions of its children. Conversely, our approach exploits regulatization constraints inspired by TKs, to drive the training phase in building significant hierarchical clusters.
